Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications
P. Vijayaragavan, Chalumuru Suresh, A Maheshwari, K Vijayalakshmi, R Narayanamoorthi, Miroslava Gono, Tomas Novak · 2024
Read Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications by P. Vijayaragavan, Chalumuru Suresh, A Maheshwari, K Vijayalakshmi, R Narayanamoorthi, Miroslava Gono, Tomas Novak (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11532552.txt.
Description
Vijayaragavan and co-authors present, in a 2024 paper, a hybrid deep learning system that reads customer sentiment in online shopping reviews. Knowing whether buyers feel positive, neutral, or negative helps stores lift satisfaction, and automatic reading must handle the tangled wording of real reviews.
Their pipeline cleans the text, turns words into vectors with word2vec, and classifies feeling with a weighted parallel model that merges two classic components. The design is framed for smart city retail platforms, where review streams are huge and constant. The authors report their approach sorts sentiment with the accuracy such services need to track public mood about products.
Catalog record
| CREATOR | P. Vijayaragavan, Chalumuru Suresh, A Maheshwari, K Vijayalakshmi, R Narayanamoorthi, Miroslava Gono, Tomas Novak |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 83 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |